Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Semantic Modeling of Outdoor Scenes for the Creation of Virtual Environments and Simulations

Efforts from both academia and industry have adopted photogrammetric techniques to generate visually compelling 3D models for the creation of virtual environments and simulations. However, such generated meshes do not contain semantic information for distinguishing between objects. To allow both user- and system-level interaction with the meshes, and enhance the visual acuity of the scene, classifying the generated point clouds and associated meshes is a necessary step. This paper presents a point cloud/mesh classification and segmentation framework. The proposed framework provides a novel way of extracting object information – i.e., individual tree locations and related features while considering the data quality issues presented in a photogrammetric-generated point cloud. A case study has been conducted using data that were collected at the University of Southern California to evaluate the proposed framework

lidR : an R package for analysis of Airborne Laser Scanning (ALS) data

Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows. Herein, we describe the philosophy behind the development of the lidR package. Implemented in the R environment with a C/C++ backend, lidR is free, open-source and cross-platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data. We review current algorithms used by the research community, and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches. Through a detailed description of the package, we address the key considerations and the design philosophy that enables users to implement user-defined tools. We also discuss algorithm choices that make the package representative of the ‘state-of-the-art' and we highlight some internal limitations through examples of processing time discrepancies. We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline

LiDAR based Biomass Estimation System for Forested Areas

In continental Portugal, forest fires are considered the biggest and most serious cause of forest deterioration and therefore the introduction of forest management mechanisms and biomass monitoring are imperative for a better future. However, conducting field studies on a large scale is a very expensive and time-consuming task. Alternatively, through remote sensing via a LiDAR, it becomes possible to map, with high accuracy, forest parameters such as tree height, diameter at breast height or tree canopy length in order to carry out other relevant estimates such as above ground biomass. In this sense, this dissertation aims to develop a system capable of, through algorithms and filters of point cloud processing, as statistical outlier removal, progressive morphological filters and region growing segmentation, extract in detail,a digital terrain model and correctly detect the number of trees in a given area, proceeding to the measurement of some interesting variables from the point of view of a forest inventory. Thus, testing data of different characteristics, our detection method obtained positive results, with all the average detection rates above 80 %.Em Portugal continental, os incêndios florestais são considerados a maior e mais grave causa de deterioramento da floresta e por isso a introdução de mecanismos de gestão florestal e monitorização da biomassa são imperativos para um futuro melhor. No entanto, realizar estudos de campo em grande escala é uma tarefa muito dispendiosa e demorosa. Em alternativa, através da deteção remota por vias de um LiDAR torna-se possível mapear, com elevado rigor, parâmetros florestais como altura das arvores, diâmetro do tronco ou comprimento da copa da arvore de modo a proceder a outras relevantes estimações como a biomassa. Neste sentido, esta dissertação teve como objetivo o desenvolvimento de um sistema capaz de, através de algoritmos e filtros de processamento de nuvens de pontos, como remoção de outliers estatístico, filtros morfologicos progressivos e segmentação por crescimento de regiões anexas , extrair com detalhe, um modelo digital do terreno e detetar corretamente o número de arvores numa determinada área, procedendo à medição de algumas variáveis interessantes do ponto de vista do inventário florestal. Assim, testando dados de diferentes características, o nosso método de deteção obteve resultados positivos, com todas as taxas deteção média superiores a 80 %

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation

Toward knowledge-based automatic 3D spatial topological modeling from LiDAR point clouds for urban areas

Le traitement d'un très grand nombre de données LiDAR demeure très coûteux et nécessite des approches de modélisation 3D automatisée. De plus, les nuages de points incomplets causés par l'occlusion et la densité ainsi que les incertitudes liées au traitement des données LiDAR compliquent la création automatique de modèles 3D enrichis sémantiquement. Ce travail de recherche vise à développer de nouvelles solutions pour la création automatique de modèles géométriques 3D complets avec des étiquettes sémantiques à partir de nuages de points incomplets. Un cadre intégrant la connaissance des objets à la modélisation 3D est proposé pour améliorer la complétude des modèles géométriques 3D en utilisant un raisonnement qualitatif basé sur les informations sémantiques des objets et de leurs composants, leurs relations géométriques et spatiales. De plus, nous visons à tirer parti de la connaissance qualitative des objets en reconnaissance automatique des objets et à la création de modèles géométriques 3D complets à partir de nuages de points incomplets. Pour atteindre cet objectif, plusieurs solutions sont proposées pour la segmentation automatique, l'identification des relations topologiques entre les composants de l'objet, la reconnaissance des caractéristiques et la création de modèles géométriques 3D complets. (1) Des solutions d'apprentissage automatique ont été proposées pour la segmentation sémantique automatique et la segmentation de type CAO afin de segmenter des objets aux structures complexes. (2) Nous avons proposé un algorithme pour identifier efficacement les relations topologiques entre les composants d'objet extraits des nuages de points afin d'assembler un modèle de Représentation Frontière. (3) L'intégration des connaissances sur les objets et la reconnaissance des caractéristiques a été développée pour inférer automatiquement les étiquettes sémantiques des objets et de leurs composants. Afin de traiter les informations incertitudes, une solution de raisonnement automatique incertain, basée sur des règles représentant la connaissance, a été développée pour reconnaître les composants du bâtiment à partir d'informations incertaines extraites des nuages de points. (4) Une méthode heuristique pour la création de modèles géométriques 3D complets a été conçue en utilisant les connaissances relatives aux bâtiments, les informations géométriques et topologiques des composants du bâtiment et les informations sémantiques obtenues à partir de la reconnaissance des caractéristiques. Enfin, le cadre proposé pour améliorer la modélisation 3D automatique à partir de nuages de points de zones urbaines a été validé par une étude de cas visant à créer un modèle de bâtiment 3D complet. L'expérimentation démontre que l'intégration des connaissances dans les étapes de la modélisation 3D est efficace pour créer un modèle de construction complet à partir de nuages de points incomplets.The processing of a very large set of LiDAR data is very costly and necessitates automatic 3D modeling approaches. In addition, incomplete point clouds caused by occlusion and uneven density and the uncertainties in the processing of LiDAR data make it difficult to automatic creation of semantically enriched 3D models. This research work aims at developing new solutions for the automatic creation of complete 3D geometric models with semantic labels from incomplete point clouds. A framework integrating knowledge about objects in urban scenes into 3D modeling is proposed for improving the completeness of 3D geometric models using qualitative reasoning based on semantic information of objects and their components, their geometric and spatial relations. Moreover, we aim at taking advantage of the qualitative knowledge of objects in automatic feature recognition and further in the creation of complete 3D geometric models from incomplete point clouds. To achieve this goal, several algorithms are proposed for automatic segmentation, the identification of the topological relations between object components, feature recognition and the creation of complete 3D geometric models. (1) Machine learning solutions have been proposed for automatic semantic segmentation and CAD-like segmentation to segment objects with complex structures. (2) We proposed an algorithm to efficiently identify topological relationships between object components extracted from point clouds to assemble a Boundary Representation model. (3) The integration of object knowledge and feature recognition has been developed to automatically obtain semantic labels of objects and their components. In order to deal with uncertain information, a rule-based automatic uncertain reasoning solution was developed to recognize building components from uncertain information extracted from point clouds. (4) A heuristic method for creating complete 3D geometric models was designed using building knowledge, geometric and topological relations of building components, and semantic information obtained from feature recognition. Finally, the proposed framework for improving automatic 3D modeling from point clouds of urban areas has been validated by a case study aimed at creating a complete 3D building model. Experiments demonstrate that the integration of knowledge into the steps of 3D modeling is effective in creating a complete building model from incomplete point clouds

Sub-canopy terrain modelling for archaeological prospecting in forested areas through multiple-echo discrete-pulse laser ranging: a case study from Chopwell Wood, Tyne & Wear

Airborne Light Detection and Ranging (LiDAR) technology is assessed for its effectiveness as a tool for measuring terrain under forest canopy. To evaluate the capability of multiple-return discrete-pulse airborne laser ranging for detecting and resolving sub-canopy archaeological features, LiDAR data were collected from a helicopter over a forest near Gateshead in July 2009. Coal mining and timber felling have characterised Chopwell Wood, a mixed coniferous and deciduous woodland of 360 hectares, since the Industrial Revolution. The state-of-the-art Optech ALTM 3100EA LiDAR system operated at 70,000 pulses per second and raw data were acquired over the study area at a point density of over 30 points per square metre. Reference terrain elevation data were acquired on-site to ‘train’ the progressive densification filtering algorithm of Axelsson (1999; 2000) to identify laser reflections from the terrain surface. A number of sites, offering a variety of tree species, variable terrain roughness & gradient and understorey vegetation cover of varying density, were identified in the wood to assess the accuracy of filtered LiDAR terrain data. Results showed that the laser scanner over-estimated the elevation of reference terrain data by 13±17 cm under deciduous canopy and 23±18 cm under coniferous canopy. Terrain point density was calculated as 4.1 and 2.4 points per square metre under deciduous and coniferous forest, respectively. Classified terrain points were modelled with the kriging interpolation technique and topographic archaeological features, such as coal tubways (transportation routes) and areas of subsidence over relic mine shafts, were identified in digital terrain models (DTMs) using advanced exaggeration and artificial illumination techniques. Airborne LiDAR is capable of recording high quality terrain data even under the most dense forest canopy, but the accuracy and density of terrain data are controlled by a combination of tree species, forest management practices and understorey vegetation